Detection and identification of poisonous gases before they have the chance to do harm is performed by various means, examples being home-use gas detectors for carbon monoxide (CO) and hydrocarbons. In many instances, detection is based on threshold amounts of present concentration being relevant to toxic levels. By way of example, a CO detector normally triggers its alarm at either the toxic level (as in a vehicle) or at a level substantially above naturally occurring ambient levels.
Other techniques which may be more sensitive to low levels of potential toxic materials include molecular spectroscopy, chemical, or biological methods. These methods are often not portable, reliable, repeatable, broad-based, or do not provide a fast response time. These methods are too slow and costly to be practical in the context of some types of detection requirements, such as detection of rapidly dispersing toxic materials and detection of materials in the military environment.
Interferometers having multiple lasers could be used for gas detection, but this approach would be more costly and less accurate, since the output might not be fused into one picture for analysis. It is important to fuse one picture because interferometric images can embody a great deal of information—including the constituent gaseous elements, their relative concentrations, and the first derivative of the composition (i.e., rate of change). Additionally, it would be extremely advantageous if the output could be quickly and accurately interpreted to interpret the presence of toxic gases without placing the human operator at risk. One way to do this would be to incorporate an automatic means, such as a neural network, to interpret the interferometer output images.